Surgical Endoscopy

, Volume 32, Issue 3, pp 1165–1173 | Cite as

Patient-specific mental rehearsal with interactive visual aids: a path worth exploring?

  • Marina Yiasemidou
  • Raffaele Galli
  • Daniel Glassman
  • Matthew Tang
  • Rahoz Aziz
  • David Jayne
  • Danilo Miskovic
Open Access



Surgeons of today are faced with unprecedented challenges; necessitating a novel approach to pre-operative preparation which takes into account the specific tests each case poses. In this study, we examine patient-specific mental rehearsal for pre-surgical practice and assess whether this method has an additional effect when compared to generic mental rehearsal.


Sixteen medical students were trained how to perform a simulated laparoscopic cholecystectomy (SLC). After baseline assessments, they were randomised to two equal groups and asked to complete three SLCs involving different anatomical variants. Prior to each procedure, Group A practiced mental rehearsal with the use of a pre-prepared checklist and Group B mental rehearsal with the checklist combined with virtual models matching the anatomical variations of the SLCs. The performance of the two groups was compared using simulator provided metrics and competency assessment tool (CAT) scoring by two blinded assessors.


The participants performed equally well when presented with a “straight-forward” anatomy [Group A vs. Group B—time sec: 445.5 vs. 496 p = 0.64—NOM: 437 vs. 413 p = 0.88—PL cm: 1317 vs. 1059 p = 0.32—per: 0.5 vs. 0 p = 0.22—NCB: 0 vs. 0 p = 0.71—DVS: 0 vs. 0 p = 0.2]; however, Group B performed significantly better [Group A vs. B Total CAT score—Short Cystic Duct (SCD): 20.5 vs. 26.31 p = 0.02 η 2 = 0.32—Double cystic Artery (DA): 24.75 vs. 30.5 p = 0.03 η 2 = 0.28] and committed less errors (Damage to Vital Structures—DVS, SCD: 4 vs. 0 p = 0.03 η 2=0.34, DA: 0 vs. 1 p = 0.02 η 2 = 0.22). in the cases with more challenging anatomies.


These results suggest that patient-specific preparation with the combination of anatomical models and mental rehearsal may increase operative quality of complex procedures.


Mental rehearsal Pre-operative preparation Patient-specific Surgical skills 

Driven by patient safety issues most western countries imposed working hours’ restrictions [1, 2] in order to reduce medical errors made by fatigued doctors working long hours [3]. Since their introduction, avoidable medical errors and adverse events have decreased [4, 5] and surgical residences’ quality of life improved [6, 7]. However, alongside working hours, training time and opportunities were condensed [3]. As a result, conventional training patterns, purely based on exposure to a rich and diverse clinical case mix has become unrealistic.

Combined with increasing technological advancements dominating contemporary surgery [8], training requirements have radically changed in the past decades [9]. Although “in-vitro” methods such as simulation were shown to successfully increase technical skills [8] methods that can increase the efficiency of available training time in a clinical environment have not been adequately explored. Techniques such as mental practice [10] or patient-specific surgical rehearsals, have been trialled for increasing efficiency and quality of surgery [11, 12, 13, 14, 15] but to this date have not gained widespread recognition.

Cognitive reproduction of a motor task without explicit physical movement, otherwise known as mental rehearsal or imagery [16, 17, 18], has been successfully used in various fields [19, 20, 21], including surgery [22, 23, 24, 25, 26, 27], for the acquisition of motor skills. The similarity of neurocognitive pathways activated during mental and real practice of a motor task is increasingly being recognised by electroencephalography studies [28, 29, 30]. The content of mental rehearsal sessions in surgery is variable. Most commonly, it takes the form of relaxation techniques followed by a step-by-step breakdown of the procedure, or a descriptive text, inclusive of visual and kinaesthetic cues [23, 24, 27, 31, 32] derived from semi-structured interviews with expert surgeons [23, 31]. This process is performed once [24, 27, 32] or repeated several times [25, 33, 34] in order to prepare for the actual surgical procedure.

Mental rehearsal does not usually involve operation-specific characteristics, which are important, as they often determine the technical difficulty of an operation. Some operation specifics (e.g. anatomical variations) can be derived from medical imagery pre-operatively and incorporated into the surgeon’s preparation, facilitating a more precise representation of intraoperative difficulties. Introduction of patient-specific elements into mental rehearsal can be readily achieved with the use of patient-specific anatomical models.

Surgical planning using patient-specific anatomical models has been sporadically applied in the past [11, 13, 15, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61] and although it is more popular in some specialties [11, 12, 35, 36, 39, 40, 41, 43, 46, 50, 51, 52, 62, 63, 64, 65], it has not penetrated into routine practice. Some of the reasons for this are the cost of associated hardware and the time required in the simulation suite [11, 12, 15]. The fusion of mental rehearsal and anatomical models does not require the use of a simulator and can be practiced repeatedly in the surgeon’s own time using a personal computer.

The authors have previously assessed the feasibility of combining mental rehearsal and patient-specific interactive anatomical models [66], but have not explored this modality within technically demanding cases. The current study aims to evaluate whether the addition of interactive case/patient-specific element to mental rehearsal can provide an additional benefit to mental rehearsal alone.


Surgical procedure

For the purposes of this exploratory study, simulated laparoscopic cholecystectomy (LC) was the procedure of choice for the following reasons: (i) virtual reality LC simulators are readily available (LapMentor®, Simbionix, Israel) [67], (ii) simulated operations with anatomical variations are provided [68], (iii) LC is a commonly performed operation involving complex laparoscopic skills [69], and (iv) the anatomy of the cystic duct and artery vary significantly, demanding varying degrees of technical competency [70].


Sixteen medical students, (years two–five and intercalating), who have never seen a laparoscopic cholecystectomy or used the virtual reality simulator before, volunteered for the study after receiving email invitation using the mailing list of the university of Leeds. Sample size calculation was based on the primary outcome for the study, the Competency Assessment Tool—CAT, a validated scoring system for assessing surgical performance, specifically designed for laparoscopic cholecystectomy [71]. A reduction in CAT score from 3 to 2 was assumed to be clinically meaningful, requiring 8 patients to be recruited to either Group A using a mental rehearsal checklist to prepare prior to simulated surgery or Group B using the same checklist and an interactive 3D anatomical model; to determine a significant difference at 80% power (α=0.05, β=0.2, Standard Deviation of 0.7).

Subjects underwent small group teaching sessions on the clinical indications, anatomy, surgical technique, and complications after a laparoscopic cholecystectomy (LC). They were shown how to use the virtual reality simulator (VRS) and taught a series of defined tasks on the simulator as well as a complete laparoscopic cholecystectomy. Subsequently, they performed 10 repetitions of the “normal anatomy” laparoscopic cholecystectomy, each at least 45 min apart from the other.

Upon conclusion of the training phase, participants completed a questionnaire assessing their ability for mental imagery (MIQ-RS) [72] and performed a simulated laparoscopic cholecystectomy, which was scored using CAT. The MIQ-RS consists of 14 tasks; trainees are initially asked to physically perform an action (e.g. raising a knee as high as possible and then lowering the knee so they are standing again on two feet) and after they are asked to visualise or to feel themselves performing the same task without overt physical movement. Subsequently, they were asked to score how easy it was to visualise or feel the task. A Likert scale (1–7, 1: Very hard to see/feel, 7: Very easy to see/feel) was used for that purpose [72]. According to the results of the MIQ-RS and CAT, they were paired in dyads of similar ability and then randomised to two equal groups (Fig. 1) through the process of a draw consisting of eight “checklist only” tickets and eight “checklist and model” tickets. Had participants within a couple drawn the same type of ticket, the process was repeated until they were randomised into two different groups. In such a manner the number of participants in each group remained equal.
Fig. 1

Study methodology. VRS virtual reality simulator, CAT competency assessment tool, MR mental rehearsal, MIQ mental imagery questionnaire, NA normal anatomy, SCD short cystic duct, DA double cystic artery

Preparation of mental rehearsal checklist

For the purposes of preparing a mental rehearsal checklist (Table 1) semi-structured interviews were conducted with five specialist surgeons who regularly perform laparoscopic cholecystectomy. The concepts of mental rehearsal, and visual and kinaesthetic cues were explained and they were asked to describe how they would perform a laparoscopic cholecystectomy.
Table 1

Mental rehearsal checklist



View model


Visualise the retracted liver and gallbladder



Decide which instruments to use and insert them into the “abdomen” under direct vision (visualise and feel)



Visualise Calot’s triangle



Retract the gallbladder (feel) in a manner that highlights Calot’s triangle (visualise the retracted gallbladder)



Decide from where and how you will commence dissection



Begin dissecting Calot’s triangle (visualise and feel)



Continue the dissection carefully exposing the cystic duct and artery while adjusting the place of the retracted gb to achieve optimal view—describe the movements of both hands (visualise and feel) and what are the end points of the dissection



Visualise the skeletonised artery and duct



Insert the clip applier under direct vision (visualise). Place firmly on the cystic duct (feel), visualise both jaws of the instrument (visualise) and then place the number of clips you wish, where you choose (visualise)



Repeat step 9 with artery—visualise the end result to ensure no complications occurred



Insert the electrocautery instrument you will use for dissecting the gall bladder off the liver bed under direct vision (visualise)



Retract the gallbladder as you see fit (visualise and feel) and commence the dissection of the gb off the liver bed from the point you choose (visualise)



Continue the dissection of the gallbladder from the liver bed adjusting the retraction position as you see fit (visualise and feel)—describe your movements



Ensure that there is no bleeding from the liver bed either right before the completion of the dissection or at the end of it (visualise)—describe how you would deal with any bleeding

The interviews were transcribed verbatim and analysed by two of the authors, conducting descriptive synthesis and extraction of visual (e.g. “I now see Calot’s triangle”) and kinaesthetic cues (e.g. “I retract the gallbladder towards the right shoulder with moderate strength”) embedded within various steps of the procedure. The most commonly occurring cues were introduced into the checklist. These were combined with the stages of the procedure most frequently described by the surgeons in order to produce a 14-step checklist (Table 1) which could be combined with visualisation of the interactive 3D models (Fig. 2). This was adjusted to the stages of the procedure which can be completed on the VRS.
Fig. 2

Virtual models A normal anatomy, B short cystic duct and C double cystic artery

3D models preparation

Three different anatomical variations were chosen for this study: “normal anatomy” (NA), “short cystic duct” (SCD) and “double cystic artery” (DA). For each anatomical variation, a 3D model was reconstructed manually from an anonymised computed tomography (CT) scan using an “in-house” 3D reconstruction software (Volume Viewer, University of Leeds). The model was exported onto open source visualisation software (MeshLab).

The NA gallbladder consisted of a normal sized cystic duct and a single cystic artery positioned posteriorly to the cystic duct. The SCD had a shorter duct and a single artery posterior to the duct. The DA gallbladder had a normal sized duct and two cystic arteries, one anterior and one posterior to the cystic duct (Fig. 2).

Intervention and comparators

During the mental rehearsal session, the subjects were seated in a quiet place and given time to relax. Participants randomised to group B were taught how to use the 3D model viewing software. All subjects were asked to read through the mental rehearsal checklist and prepare to verbalise how they would perform the procedure whilst “viewing” and “feeling” the operation (visual and kinaesthetic cues) based on their previous experience of performing the procedure on the simulator.

The participants randomised to group A (n = 8) were asked to perform a Normal Anatomy (NA) simulated LC, a Short Cystic Duct (SCD) and a Double cystic Artery (DA) simulated LC after completing a mental rehearsal session with the use of the checklist only. The students randomised to group B (n = 8) were asked to do the same, but for most steps on the checklist (indicated with an asterisk—Table 1) they were also asked to review the appropriate anatomical model. Group A was informed of the anatomical variation of the eminent procedure, but did not have access to the relevant anatomical model provided to group B. This process was repeated before every simulated procedure. All procedures were video-recorded for later assessment.

Measured outcomes

Performance (time, Number Of Movements—NOM and Path Length—PL) and safety metrics (Number of perforations—Per, number of Non-Cauterised Bleeding—NCB and number of Damages to Vital Structures—DVS) automatically provided by the VRS were compared between the two groups for each type of anatomy. Proficiency gain curves for time to complete the procedure (time), Number Of Movements [73] and Path Length (PL) of the instrumental tip were generated by curve fitting raw data using power law [f(x) = axk − a: first attempt result and k: log of learning rate divided by log of 2] [74].

The recordings of the procedure were judged by two blinded assessors [R.G, D.G] using the competency assessment tool designed specifically for laparoscopic cholecystectomy [71]. The initial category of this score refers to the insertion of ports and as this was not part of the VRS, this category was not used for scoring.

Statistical analysis

The unpaired t-test was used to compare continuous data and the Mann–Whitney U-test for discrete data. Eta squared is reported for the statistically significant outcomes (p < 0.05). IBM® SPSS® Statistics Vs. 24 and GraphPad Prism® 7.0b, GraphPad Software, Inc. were used for all statistical analysis and preparation of graphs. Agreement between assessors was evaluated using the Intraclass Correlation Coefficient (ICC).


The baseline ability of the two groups was similar (Fig. 3). Proficiency gain curves demonstrated that medical students experienced a learning effect prior to embarking on the comparative part of the study (Fig. 4).
Fig. 3

Baseline ability of the two groups. MIQ mental imagery questionnaire, CAT competency assessment tool. Y-axis demonstrates mean values for each variable indicated in the X-axis and error bars show SEM (standard error of mean)

Fig. 4

Learning curves for initial 10 LCs

VRS performance and safety metrics

Normal anatomy

There was no statistical difference in performance [checklist vs. model—time (s): 445.5 vs. 496 p = 0.64—NOM: 437 vs. 413 p = 0.88 – PL [75]: 1317 vs. 1059 p = 0.32] or safety metrics [checklist vs. model—per: 0.5 vs. 0 p = 0.22—NCB: 0 vs. 0 p = 0.71—DVS: 0 vs. 0 p = 0.2] between the two groups (Fig. 5).
Fig. 5

VRS metrics. NOM number of movements, PL path length. PL is measured in cm and time in secs

Short cystic artery

There was no statistical difference in all metrics but the number of damage to vital structures that was significantly greater in the Group A [checklist vs. model—time (s): 464.3 vs. 555 p = 0.2—NOM: 506 vs. 481 p = 0.86—PL [75]: 1363 vs. 1118 p = 0.17—per: 0.5 vs. 0 p = 0.13—NCB: 0 vs. 0 p = 0.2—DVS: 4 vs. 0 p = 0.03 η 2 = 0.34] (Fig. 5).

Double cystic artery

The only parameter that showed a significant difference was the number of damage to vital structures in Group A [checklist vs. model—time (s): 498.4 vs. 565.8 p = 0.43—NOM: 541.5 vs. 514.5 p = 0.4—PL [75]: 1385 vs. 1171 p = 0.07—per: 0.5 vs. 0 p = 0.28—NCB: 0 vs. 0 p > 1—DVS: 1 vs. 0 p = 0.02 η 2 = 0.22] (Fig. 5).

CAT score

The two assessors of the LC videos were in good agreement with each other [ICC: 0.81—95% CI (0.66–0.89)]. According to the CAT scores, Group B performed the SCD and DA LC significantly better than the Group A, but there was no statistically significant difference in the performance of the NA LC [checklist vs. model total CAT score—NA: 23.63 vs. 26.69 p = 0.2—SCD: 20.5 vs. 26.31 p = 0.02 η 2 = 0.32—DA: 24.75 vs. 30.5 p = 0.03 η 2 = 0.28] (Fig. 6).
Fig. 6

Competency assessment tool scores


To our knowledge this is the first study testing patient-specific mental rehearsal with the use of 3D interactive visual aids. The results show that both groups performed equally well when given “straight-forward” anatomy that they had encountered before. The group who used patient-specific anatomical models as well as the mental rehearsal checklist performed significantly better (CAT scores) and committed less errors (DVS) in cases with more challenging anatomies (i.e. short cystic duct and double cystic artery). These results support further investigation into the application of patient-specific preparation with the combination of anatomical models and mental rehearsal, within a clinical environment.

The methodology used in this study is aligned to that described in the literature for mental rehearsal [10, 22, 27, 32]. Experts were consulted to create a mental rehearsal checklist and an extensive step-by-step breakdown and teaching and training were provided to the participants prior to the intervention. The performance metrics have been previously validated for demonstrating surgical competency [76]. However, PL and NOM are indicative of economy of movements and any difference in these values may not translate into differences in the safety aspect of the procedure [71]. Similarly, time to complete a procedure is frequently associated with competency [76, 77, 78, 79], but not necessarily with quality [71]. This is mirrored in the results of the study, showing completion of the SCD and DA cases in a similar amount of time, whilst Group A had significantly lower CAT score and higher number of damage to vital structures. This justifies the addition of three safety measures (number of perforations, non-cauterised bleeding and damage to vital structures) and the CAT score evaluation as outcome measures. The assessor using CAT score has the opportunity to comment on hazardous use of instruments or detrimental tissue handling, near misses and errors as well as the fluency of the performed operation [71].

This study has some limitations. First, the participants were medical students and not surgeons, which has implications for generalisability. Due to the time commitment needed for the study, it is likely that recruitment of surgical trainees would have resulted in a high drop-out rate, a frequent problem with educational studies [80, 81, 82]. Although the authors recognise that medical students are not the target group of the suggested intervention, every possible effort was made to maintain uniform experience and baseline ability of participants (Fig. 3). Second, the study was not conducted in a clinical environment but in a simulation suite. Whilst the VRS used in this study has good validity [83, 84] and skills gained using such simulators are transferable to the operating room [79], there are intrinsic differences between a simulated and a real procedure [71]. This is reflected in the minor modifications needed for the CAT score and mental rehearsal checklist to extract the parts of the procedure not portrayed on the simulator (e.g. insertion of ports or patient positioning). Having established a possible benefit to mental rehearsal combined with patient-specific anatomical models in a simulated environment, the next step is to test the intervention within a clinical randomised controlled trial. The participants in the future trial should be surgical trainees.


The combination of mental rehearsal and patient-specific anatomical models reduces error occurrence and improves quality of surgery in complex procedures undertaken within a simulated environment.



The authors thank Heather Ricketts for transcribing some of the interviews with experts conducted during this project.


This project was kindly funded by Leeds Teaching Hospitals Charitable Foundation. Dr Yiasemidou was the recipient of the A.G Leventis Foundation Scholarship.

Compliance with ethical standards


Marina Yiasemidou, Raffaele Galli, Daniel Glassman, Matthew Tang, Rahoz Aziz, David Jayne, Danilo Miskovic have no conflicts of interest or financial ties to disclose.


  1. 1.
    Fitzgerald JE, Caesar BC (2012) The European working time directive: a practical review for surgical trainees. Int J Surg 10:399–403CrossRefPubMedGoogle Scholar
  2. 2.
    Iglehart JK (2008) Revisiting duty-hour limits–IOM recommendations for patient safety and resident education. N Engl J Med 359:2633–2635CrossRefPubMedGoogle Scholar
  3. 3.
    Hopmans CJ, den Hoed PT, van der Laan L, van der Harst E, van der Elst M, Mannaerts GH, Dawson I, Timman R, Wijnhoven BP, IJzermans JN (2015) Impact of the European working time directive (EWTD) on the operative experience of surgery residents. Surgery 157:634–641CrossRefPubMedGoogle Scholar
  4. 4.
    Barger LK, Ayas NT, Cade BE, Cronin JW, Rosner B, Speizer FE, Czeisler CA (2006) Impact of extended-duration shifts on medical errors, adverse events, and attentional failures. PLoS Med 3:e487CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Landrigan CP, Rothschild JM, Cronin JW, Kaushal R, Burdick E, Katz JT, Lilly CM, Stone PH, Lockley SW, Bates DW, Czeisler CA (2004) Effect of reducing interns’ work hours on serious medical errors in intensive care units. N Engl J Med 351:1838–1848CrossRefPubMedGoogle Scholar
  6. 6.
    Curet MJ (2008) Resident work hour restrictions: where are we now? J Am Coll Surg 207:767–776CrossRefPubMedGoogle Scholar
  7. 7.
    Gopal R, Glasheen JJ, Miyoshi TJ, Prochazka AV (2005) Burnout and internal medicine resident work-hour restrictions. Arch Intern Med 165:2595–2600CrossRefPubMedGoogle Scholar
  8. 8.
    Stefanidis D, Sevdalis N, Paige J, Zevin B, Aggarwal R, Grantcharov T, Jones DB, Association for Surgical Education Simulation C (2015) Simulation in surgery: what’s needed next? Ann Surg 261:846–853CrossRefPubMedGoogle Scholar
  9. 9.
    Gawande A (2012) Two hundred years of surgery. N Engl J Med 366:1716–1723CrossRefPubMedGoogle Scholar
  10. 10.
    Rao A, Tait I, Alijani A (2015) Systematic review and meta-analysis of the role of mental training in the acquisition of technical skills in surgery. Am J Surg 210:545–553CrossRefPubMedGoogle Scholar
  11. 11.
    Desender L, Rancic Z, Aggarwal R, Duchateau J, Glenck M, Lachat M, Vermassen F, Van Herzeele I, Everest (2013) Patient-specific rehearsal prior to EVAR: a pilot study. Eur J Vasc Endovasc Surg 45:639–647CrossRefPubMedGoogle Scholar
  12. 12.
    Hislop SJ, Hedrick JH, Singh MJ, Rhodes JM, Gillespie DL, Johansson M, Illig KA (2009) Simulation case rehearsals for carotid artery stenting. Eur J Vasc Endovasc Surg 38:750–754CrossRefPubMedGoogle Scholar
  13. 13.
    Perry M, Banks P, Richards R, Friedman EP, Shaw P (1998) The use of computer-generated three-dimensional models in orbital reconstruction. Br J Oral Maxillofac Surg 36:275–284CrossRefPubMedGoogle Scholar
  14. 14.
    Willaert W, Aggarwal R, Harvey K, Cochennec F, Nestel D, Darzi A, Vermassen F, Cheshire N, European Virtual Reality Endovascular Research T (2011) Efficient implementation of patient-specific simulated rehearsal for the carotid artery stenting procedure: part-task rehearsal. Eur J Vasc Endovasc Surg 42:158–166CrossRefPubMedGoogle Scholar
  15. 15.
    Willaert WI, Aggarwal R, Van Herzeele I, Plessers M, Stroobant N, Nestel D, Cheshire N, Vermassen F (2012) Role of patient-specific virtual reality rehearsal in carotid artery stenting. Br J Surg 99:1304–1313CrossRefPubMedGoogle Scholar
  16. 16.
    Bernardi NF, De Buglio M, Trimarchi PD, Chielli A, Bricolo E (2013) Mental practice promotes motor anticipation: evidence from skilled music performance. Front Hum Neurosci 7:451CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Jackson TD, Kaplan GG, Arena G, Page JH, Rogers SO Jr (2007) Laparoscopic versus open resection for colorectal cancer: a metaanalysis of oncologic outcomes. J Am Coll Surg 204:439–446CrossRefPubMedGoogle Scholar
  18. 18.
    Jeannerod M (1995) Mental imagery in the motor context. Neuropsychologia 33:1419–1432CrossRefPubMedGoogle Scholar
  19. 19.
    Deschaumes-Molinaro C, Dittmar A, Vernet-Maury E (1991) Relationship between mental imagery and sporting performance. Behav Brain Res 45:29–36CrossRefPubMedGoogle Scholar
  20. 20.
    Lotze M, Scheler G, Tan HR, Braun C, Birbaumer N (2003) The musician’s brain: functional imaging of amateurs and professionals during performance and imagery. Neuroimage 20:1817–1829CrossRefPubMedGoogle Scholar
  21. 21.
    Mendoza D, Wichman H (1978) “Inner” darts: effects of mental practice on performance of dart throwing. Percept Mot Skills 47:1195–1199CrossRefPubMedGoogle Scholar
  22. 22.
    Arora S, Aggarwal R, Moran A, Sirimanna P, Crochet P, Darzi A, Kneebone R, Sevdalis N (2011) Mental practice: effective stress management training for novice surgeons. J Am Coll Surg 212:225–233CrossRefPubMedGoogle Scholar
  23. 23.
    Arora S, Aggarwal R, Sirimanna P, Moran A, Grantcharov T, Kneebone R, Sevdalis N, Darzi A (2011) Mental practice enhances surgical technical skills: a randomized controlled study. Ann Surg 253:265–270CrossRefPubMedGoogle Scholar
  24. 24.
    Eldred-Evans D, Grange P, Cheang A, Yamamoto H, Ayis S, Mulla M, Immenroth M, Sharma D, Reedy G (2013) Using the mind as a simulator: a randomized controlled trial of mental training. J Surg Educ 70:544–551CrossRefPubMedGoogle Scholar
  25. 25.
    Immenroth M, Burger T, Brenner J, Nagelschmidt M, Eberspacher H, Troidl H (2007) Mental training in surgical education: a randomized controlled trial. Ann Surg 245:385–391CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Louridas M, Bonrath EM, Sinclair DA, Dedy NJ, Grantcharov TP (2015) Randomized clinical trial to evaluate mental practice in enhancing advanced laparoscopic surgical performance. Br J Surg 102:37–44CrossRefPubMedGoogle Scholar
  27. 27.
    Sanders CW, Sadoski M, van Walsum K, Bramson R, Wiprud R, Fossum TW (2008) Learning basic surgical skills with mental imagery: using the simulation centre in the mind. Med Educ 42:607–612CrossRefPubMedGoogle Scholar
  28. 28.
    Beisteiner R, Hollinger P, Lindinger G, Lang W, Berthoz A (1995) Mental representations of movements. Brain potentials associated with imagination of hand movements. Electroencephalogr Clin Neurophysiol 96:183–193CrossRefPubMedGoogle Scholar
  29. 29.
    Caldara R, Deiber MP, Andrey C, Michel CM, Thut G, Hauert CA (2004) Actual and mental motor preparation and execution: a spatiotemporal ERP study. Exp Brain Res 159:389–399CrossRefPubMedGoogle Scholar
  30. 30.
    Cunnington R, Iansek R, Bradshaw JL, Phillips JG (1996) Movement-related potentials associated with movement preparation and motor imagery. Exp Brain Res 111:429–436CrossRefPubMedGoogle Scholar
  31. 31.
    Arora SAR, Sevdalis N, Moran A, Sirimanna P, Kneebone R, Darzi A (2009) Development and validation of mental practice as a training strategy for laparoscopic surgery. Surg Endosc 24:179–187CrossRefPubMedGoogle Scholar
  32. 32.
    Sanders CW, Sadoski M, Bramson R, Wiprud R, Van Walsum K (2004) Comparing the effects of physical practice and mental imagery rehearsal on learning basic surgical skills by medical students. Am J Obstet Gynecol 191:1811–1814CrossRefPubMedGoogle Scholar
  33. 33.
    Bathalon S, Dorion D, Darveau S, Martin M (2005) Cognitive skills analysis, kinesiology, and mental imagery in the acquisition of surgical skills. J Otolaryngol 34:328–332CrossRefPubMedGoogle Scholar
  34. 34.
    Jungmann F, Gockel I, Hecht H, Kuhr K, Rasanen J, Sihvo E, Lang H (2011) Impact of perceptual ability and mental imagery training on simulated laparoscopic knot-tying in surgical novices using a Nissen fundoplication model. Scand J Surg 100:78–85CrossRefPubMedGoogle Scholar
  35. 35.
    Derand P, Rannar LE, Hirsch JM (2012) Imaging, virtual planning, design, and production of patient-specific implants and clinical validation in craniomaxillofacial surgery. Craniomaxillofac Trauma Reconstr 5:137–144CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Dhanda J, Cooper C, Ellis D, Speculand B (2011) Technique of temporomandibular joint replacement using a patient-specific reconstruction system in the edentulous patient. Br J Oral Maxillofac Surg 49:618–622CrossRefPubMedGoogle Scholar
  37. 37.
    Essig H, Rana M, Kokemueller H, von See C, Ruecker M, Tavassol F, Gellrich NC (2011) Pre-operative planning for mandibular reconstruction - a full digital planning workflow resulting in a patient specific reconstruction. Head Neck Oncol 3:45CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Fornaro J, Keel M, Harders M, Marincek B, Szekely G, Frauenfelder T (2010) An interactive surgical planning tool for acetabular fractures: initial results. J Orthop Surg Res 5:50CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Franceschi JP, Sbihi A, Surgery Computer Assisted Orthopedic, Computer Assisted Orthopedic Surgery F (2014) 3D templating and patient-specific cutting guides (knee-plan) in total knee arthroplasty: postoperative CT-based assessment of implant positioning. Orthop Traumatol Surg Res 100:S281–286CrossRefPubMedGoogle Scholar
  40. 40.
    Furnstahl P, Vlachopoulos L, Schweizer A, Fucentese SF, Koch PP (2015) Complex osteotomies of tibial plateau malunions using computer-assisted planning and patient-specific surgical guides. J Orthop Trauma 29:e270–276CrossRefPubMedGoogle Scholar
  41. 41.
    Gander T, Essig H, Metzler P, Lindhorst D, Dubois L, Rucker M, Schumann P (2015) Patient specific implants (PSI) in reconstruction of orbital floor and wall fractures. J Craniomaxillofac Surg 43:126–130CrossRefPubMedGoogle Scholar
  42. 42.
    Gateno J, Xia JJ, Teichgraeber JF, Christensen AM, Lemoine JJ, Liebschner MA, Gliddon MJ, Briggs ME (2007) Clinical feasibility of computer-aided surgical simulation (CASS) in the treatment of complex cranio-maxillofacial deformities. J Oral Maxillofac Surg 65:728–734CrossRefPubMedGoogle Scholar
  43. 43.
    Haq J, Patel N, Weimer K, Matthews NS (2014) Single stage treatment of ankylosis of the temporomandibular joint using patient-specific total joint replacement and virtual surgical planning. Br J Oral Maxillofac Surg 52:350–355CrossRefPubMedGoogle Scholar
  44. 44.
    Hu Y, Li H, Qiao G, Liu H, Ji A, Ye F (2011) Computer-assisted virtual surgical procedure for acetabular fractures based on real CT data. Injury 42:1121–1124CrossRefPubMedGoogle Scholar
  45. 45.
    Isotani S, Shimoyama H, Yokota I, China T, Hisasue S, Ide H, Muto S, Yamaguchi R, Ukimura O, Horie S (2015) Feasibility and accuracy of computational robot-assisted partial nephrectomy planning by virtual partial nephrectomy analysis. Int J Urol 22:439–446CrossRefPubMedGoogle Scholar
  46. 46.
    Issa K, Rifai A, McGrath MS, Callaghan JJ, Wright C, Malkani AL, Mont MA, McInerney VK (2013) Reliability of templating with patient-specific instrumentation in total knee arthroplasty. The Journal of Knee Surgery 26:429–433CrossRefPubMedGoogle Scholar
  47. 47.
    Kanzaki M, Maeda H, Wachi N, Kikkawa T, Komine H, Isaka T, Oyama K, Murasugi M, Onuki T (2013) Complete video-assisted thoracoscopic multi-subsegmentectomy based on patients’ specific virtual 3-D pulmonary models. Asian J Endosc Surg 6:110–115CrossRefPubMedGoogle Scholar
  48. 48.
    Kockro RA, Serra L, Tseng-Tsai Y, Chan C, Yih-Yian S, Gim-Guan C, Lee E, Hoe LY, Hern N, Nowinski WL (2000) Planning and simulation of neurosurgery in a virtual reality environment. Neurosurgery 46:118–135CrossRefPubMedGoogle Scholar
  49. 49.
    Kusaka M, Sugimoto M, Fukami N, Sasaki H, Takenaka M, Anraku T, Ito T, Kenmochi T, Shiroki R, Hoshinaga K (2015) Initial experience with a tailor-made simulation and navigation program using a 3-D printer model of kidney transplantation surgery. Transpl Proc 47:596–599CrossRefGoogle Scholar
  50. 50.
    Leeuwen JA, Grogaard B, Nordsletten L, Rohrl SM (2015) Comparison of planned and achieved implant position in total knee arthroplasty with patient-specific positioning guides. Acta Orthop 86:201–207CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Leong JL, Batra PS, Citardi MJ (2005) Three-dimensional computed tomography angiography of the internal carotid artery for preoperative evaluation of sinonasal lesions and intraoperative surgical navigation. Laryngoscope 115:1618–1623CrossRefPubMedGoogle Scholar
  52. 52.
    Lo LJ, Chen YR, Tseng CS, Lee MY (2004) Computer-aided reconstruction of traumatic fronto-orbital osseous defects: aesthetic considerations. Chang Gung Medical Journal 27:283–291PubMedGoogle Scholar
  53. 53.
    Qiu TM, Zhang Y, Wu JS, Tang WJ, Zhao Y, Pan ZG, Mao Y, Zhou LF (2010) Virtual reality presurgical planning for cerebral gliomas adjacent to motor pathways in an integrated 3-D stereoscopic visualization of structural MRI and DTI tractography. Acta Neurochir (Wien) 152:1847–1857CrossRefGoogle Scholar
  54. 54.
    Radecka E, Brehmer M, Holmgren K, Palm G, Magnusson P, Magnusson A (2006) Pelvicaliceal biomodeling as an aid to achieving optimal access in percutaneous nephrolithotripsy. J Endourol 20:92–101CrossRefPubMedGoogle Scholar
  55. 55.
    Schepers RH, Raghoebar GM, Vissink A, Stenekes MW, Kraeima J, Roodenburg JL, Reintsema H, Witjes MJ (2015) Accuracy of fibula reconstruction using patient-specific CAD/CAM reconstruction plates and dental implants: a new modality for functional reconstruction of mandibular defects. J Craniomaxillofac Surg 43:649–657CrossRefPubMedGoogle Scholar
  56. 56.
    Schweizer A, Furnstahl P, Nagy L (2013) Three-dimensional correction of distal radius intra-articular malunions using patient-specific drill guides. J Hand Surg 38:2339–2347CrossRefGoogle Scholar
  57. 57.
    Scolozzi P (2012) Maxillofacial reconstruction using polyetheretherketone patient-specific implants by “mirroring” computational planning. Aesthetic Plast Surg 36:660–665CrossRefPubMedGoogle Scholar
  58. 58.
    Shen F, Chen B, Guo Q, Qi Y, Shen Y (2013) Augmented reality patient-specific reconstruction plate design for pelvic and acetabular fracture surgery. Int J Comput Assist Radiol Surg 8:169–179CrossRefPubMedGoogle Scholar
  59. 59.
    Tepper OM, Sorice S, Hershman GN, Saadeh P, Levine JP, Hirsch D (2011) Use of virtual 3-dimensional surgery in post-traumatic craniomaxillofacial reconstruction. J Oral Maxillofac Surg 69:733–741CrossRefPubMedGoogle Scholar
  60. 60.
    van Steenberghe D, Glauser R, Blomback U, Andersson M, Schutyser F, Pettersson A, Wendelhag I (2005) A computed tomographic scan-derived customized surgical template and fixed prosthesis for flapless surgery and immediate loading of implants in fully edentulous maxillae: a prospective multicenter study. Clin Implant Dent Relat Res 7(Suppl 1):S111–120CrossRefPubMedGoogle Scholar
  61. 61.
    Xia JJ, Gateno J, Teichgraeber JF, Christensen AM, Lasky RE, Lemoine JJ, Liebschner MA (2007) Accuracy of the computer-aided surgical simulation (CASS) system in the treatment of patients with complex craniomaxillofacial deformity: a pilot study. J Oral Maxillofac Surg 65:248–254CrossRefPubMedGoogle Scholar
  62. 62.
    Adolphs N, Liu W, Keeve E, Hoffmeister B (2014) RapidSplint: virtual splint generation for orthognathic surgery - results of a pilot series. Comput Aided Surg 19:20–28CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Ayoub N, Ghassemi A, Rana M, Gerressen M, Riediger D, Holzle F, Modabber A (2014) Evaluation of computer-assisted mandibular reconstruction with vascularized iliac crest bone graft compared to conventional surgery: a randomized prospective clinical trial. Trials 15:114CrossRefPubMedPubMedCentralGoogle Scholar
  64. 64.
    Dong Y, Dong Y, Hu G, Xu Q (2011) Three-dimensional reconstruction of extremity tumor regions by CT and MRI image data fusion for subject-specific preoperative assessment and planning. Comput Aided Surg 16:220–233CrossRefPubMedGoogle Scholar
  65. 65.
    Hsu SS, Gateno J, Bell RB, Hirsch DL, Markiewicz MR, Teichgraeber JF, Zhou X, Xia JJ (2013) Accuracy of a computer-aided surgical simulation protocol for orthognathic surgery: a prospective multicenter study. J Oral Maxillofac Surg 71:128–142CrossRefPubMedGoogle Scholar
  66. 66.
    Yiasemidou M, Glassman D, Mushtaq F, Athanasiou C, Williams MM, Jayne D, Miskovic D (2017) Mental practice with interactive 3D visual aids enhances surgical performance. Surg EndoscGoogle Scholar
  67. 67.
    Reichenbach DJ, Tackett AD, Harris J, Camacho D, Graviss EA, Dewan B, Vavra A, Stiles A, Fisher WE, Brunicardi FC, Sweeney JF (2006) Laparoscopic colon resection early in the learning curve: what is the appropriate setting? Ann Surg 243:730–735 (discussion 735–737) CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    (2014) Simbionix LAP MENTOR™. Laparoscopy Virtual Reality Simulator. LAP Mentor - Laparoscopy Simulator from Simbionix
  69. 69.
    Giulea C, Enciu O, Birca T, Miron A (2016) Selective Intraoperative Cholangiography in Laparoscopic Cholecystectomy. Chirurgia (Bucur) 111:26–32Google Scholar
  70. 70.
    Nagral S (2005) Anatomy relevant to cholecystectomy. J Minim Access Surg 1:53–58CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    Cole SJ, Mackenzie H, Ha J, Hanna GB, Miskovic D (2014) Randomized controlled trial on the effect of coaching in simulated laparoscopic training. Surg Endosc 28:979–986CrossRefPubMedGoogle Scholar
  72. 72.
    Gregg MHC, Butler A (2010) The MIQ-RS: a Suitable Option for Examining Movement Imagery Ability. Evid Based Complement Alternat Med 7:249–257CrossRefPubMedGoogle Scholar
  73. 73.
    Akagi T, Inomata M, Etoh T, Moriyama H, Yasuda K, Shiraishi N, Eshima N, Kitano S (2012) Multivariate evaluation of the technical difficulties in performing laparoscopic anterior resection for rectal cancer. Surg Laparosc Endosc Percutan Tech 22:52–57CrossRefPubMedGoogle Scholar
  74. 74.
    Liao S (1988) The learning curve: Wright’s model vs. Crawford’s model. Issues In Accounting Education (Fall):302–315Google Scholar
  75. 75.
    Chae MP, Rozen WM, McMenamin PG, Findlay MW, Spychal RT, Hunter-Smith DJ (2015) Emerging applications of bedside 3D printing in plastic surgery. Front Surg 2:25CrossRefPubMedPubMedCentralGoogle Scholar
  76. 76.
    Aggarwal R, Crochet P, Dias A, Misra A, Ziprin P, Darzi A (2009) Development of a virtual reality training curriculum for laparoscopic cholecystectomy. Br J Surg 96:1086–1093CrossRefPubMedGoogle Scholar
  77. 77.
    Aggarwal R, Moorthy K, Darzi A (2004) Laparoscopic skills training and assessment. Br J Surg 91:1549–1558CrossRefPubMedGoogle Scholar
  78. 78.
    Loukas C, Nikiteas N, Kanakis M, Georgiou E (2011) The contribution of simulation training in enhancing key components of laparoscopic competence. Am Surg 77:708–715PubMedGoogle Scholar
  79. 79.
    Sturm LP, Windsor JA, Cosman PH, Cregan P, Hewett PJ, Maddern GJ (2008) A systematic review of skills transfer after surgical simulation training. Ann Surg 248:166–179CrossRefPubMedGoogle Scholar
  80. 80.
    Ahlberg G, Enochsson L, Gallagher AG, Hedman L, Hogman C, McClusky DA 3rd, Ramel S, Smith CD, Arvidsson D (2007) Proficiency-based virtual reality training significantly reduces the error rate for residents during their first 10 laparoscopic cholecystectomies. Am J Surg 193:797–804CrossRefPubMedGoogle Scholar
  81. 81.
    Grantcharov TP, Kristiansen VB, Bendix J, Bardram L, Rosenberg J, Funch-Jensen P (2004) Randomized clinical trial of virtual reality simulation for laparoscopic skills training. Br J Surg 91:146–150CrossRefPubMedGoogle Scholar
  82. 82.
    Munz Y, Kumar BD, Moorthy K, Bann S, Darzi A (2004) Laparoscopic virtual reality and box trainers: is one superior to the other? Surg Endosc 18:485–494CrossRefPubMedGoogle Scholar
  83. 83.
    Ayodeji ID, Schijven M, Jakimowicz J, Greve JW (2007) Face validation of the Simbionix LAP Mentor virtual reality training module and its applicability in the surgical curriculum. Surg Endosc 21:1641–1649CrossRefPubMedGoogle Scholar
  84. 84.
    Ayodeji ID, Schijven MP, Jakimowicz JJ (2006) Determination of face validity for the Simbionix LAP mentor virtual reality training module. Stud Health Technol Inform 119:28–30PubMedGoogle Scholar

Copyright information

© The Author(s) 2017

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Leeds Institute of Biomedical and Clinical SciencesUniversity of LeedsLeedsUK
  2. 2.John Goligher Surgery UnitSt. James University HospitalLeedsUK
  3. 3.Bradford Teaching HospitalsBradfordUK
  4. 4.Medical SchoolUniversity of LeedsLeedsUK

Personalised recommendations